The semiconductor manufacturing process has become more sophisticated and expensive with each new technology node. Hence, in order to amortize the cost of fabricated chips, a very high production volume per design is usually required over an extended period of time. During this time of high volume manufacturing, it is common to have unexpected and sudden drops in the yield below normal baseline levels. This phenomenon is commonly referred to as a yield excursion. Yield excursions may happen due to various reasons like changes in fabrication equipment, changes in process parameters etc., and when this happens, it is desirable to quickly identify the source that is causing the yield to drop below normal and fix it.
Sometimes the cause of these excursions can be identified based on wafer histories, analysis of process history, etc. However, in many cases, these methods may not produce an answer. In such cases, one method that is often used is to select a small number of die from a low yielding wafer (which is referred to as an excursion wafer in this disclosure) and determine the defect in the die using physical failure analysis (PFA). However, this is an expensive and time consuming process. Moreover, it can normally be done for only a small number of failing die, which implies that the results may still not be conclusive.
Recently there has been an increasing trend toward analyzing results of logic diagnosis on production test fails in order to identify yield issues. It is desirable to do the same for identifying the cause of yield excursions, as this would help result in an overall cheaper and faster process. The use of diagnosis results to identify and rank systematic yield limiters for a particular design/process has been described in several previous studies. However, these studies are geared towards analyzing large populations, typically consisting of thousands of failing die over several manufacturing lots. Desirably, however, the cause of the yield excursion is determined from a relatively small number of die, typically a few hundred die from a single wafer. Moreover, unlike the scenarios described in previous studies, yield excursions are most commonly caused by a single cause and it is desirable to identify this dominant failing mechanism rather than to identify and rank various systematic yield limiters. For these reasons, conventional analysis techniques are not suitable for dealing with yield excursions.